OpenRaiser/Intern-Atlas huggingface dataset:
Trick the Body Trick the Mind: Avatar representation affects the perception of available action possibilities in Virtual Reality
Venue: arXiv cs.HC 2020
Year: 2020

In immersive Virtual Reality (VR), your brain can trick you into believing that your virtual hands are your real hands. Manipulating the representation of the body, namely the avatar, is a potentially powerful tool for the design of innovative interactive systems in VR. In this study, we investigated interactive behavior in VR by using the methods of experimental psychology. Objects with handles are known to potentiate the afforded action. Participants tend to respond faster when the handle is on the same side as the responding hand in bi-manual speed response tasks. In the first experiment, we successfully replicated this affordance effect in a Virtual Reality (VR) setting. In the second experiment, we showed that the affordance effect was influenced by the avatar, which was manipulated by two different hand types: 1) hand models with full finger tracking that are able to grasp objects, and 2) capsule-shaped -- fingerless -- hand models that are not able to grasp objects. We found that less than 5 minutes of adaptation to an avatar, significantly altered the affordance perception. Counter intuitively, action planning was significantly shorter with the hand model that is not able to grasp. Possibly, fewer action possibilities provided an advantage in processing time. The presence of a handle speeded up the initiation of the hand movement but slowed down the action completion because of ongoing action planning. The results were examined from a multidisciplinary perspective and the design implications for VR applications were discussed.
U2-ONet: A Two-level Nested Octave U-structure with Multiscale Attention Mechanism for Moving Instances Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Most scenes in practical applications are dynamic scenes containing moving objects, so segmenting accurately moving objects is crucial for many computer vision applications. In order to efficiently segment out all moving objects in the scene, regardless of whether the object has a predefined semantic label, we propose a two-level nested Octave U-structure network with a multiscale attention mechanism called U2-ONet. Each stage of U2-ONet is filled with our newly designed Octave ReSidual U-block (ORSU) to enhance the ability to obtain more context information at different scales while reducing spatial redundancy of feature maps. In order to efficiently train our multi-scale deep network, we introduce a hierarchical training supervision strategy that calculates the loss at each level while adding a knowledge matching loss to keep the optimization consistency. Experimental results show that our method achieves state-of-the-art performance in several general moving objects segmentation datasets.
Digital Surveillance Systems for Tracing COVID-19: Privacy and Security Challenges with Recommendations
Venue: arXiv cs.CR 2020
Year: 2020

Coronavirus disease 2019, i.e. COVID-19 has imposed the public health measure of keeping social distancing for preventing mass transmission of COVID-19. For monitoring the social distancing and keeping the trace of transmission, we are obligated to develop various types of digital surveillance systems, which include contact tracing systems and drone-based monitoring systems. Due to the inconvenience of manual labor, traditional contact tracing systems are gradually replaced by the efficient automated contact tracing applications that are developed for smartphones. However, the commencement of automated contact tracing applications introduces the inevitable privacy and security challenges. Nevertheless, unawareness and/or lack of smartphone usage among mass people lead to drone-based monitoring systems. These systems also invite unwelcomed privacy and security challenges. This paper discusses the recently designed and developed digital surveillance system applications with their protocols deployed in several countries around the world. Their privacy and security challenges are discussed as well as analyzed from the viewpoint of privacy acts. Several recommendations are suggested separately for automated contact tracing systems and drone-based monitoring systems, which could further be explored and implemented afterwards to prevent any possible privacy violation and protect an unsuspecting person from any potential cyber attack.
Dimensionality Reduction for $k$-means Clustering
Venue: arXiv cs.LG 2020
Year: 2020

We present a study on how to effectively reduce the dimensions of the $k$-means clustering problem, so that provably accurate approximations are obtained. Four algorithms are presented, two \textit{feature selection} and two \textit{feature extraction} based algorithms, all of which are randomized.
Bid Prediction in Repeated Auctions with Learning
Venue: arXiv cs.GT 2020
Year: 2020

We consider the problem of bid prediction in repeated auctions and evaluate the performance of econometric methods for learning agents using a dataset from a mainstream sponsored search auction marketplace. Sponsored search auctions is a billion dollar industry and the main source of revenue of several tech giants. A critical problem in optimizing such marketplaces is understanding how bidders will react to changes in the auction design. We propose the use of no-regret based econometrics for bid prediction, modeling players as no-regret learners with respect to a utility function, unknown to the analyst. We propose new econometric approaches to simultaneously learn the parameters of a player's utility and her learning rule, and apply these methods in a real-world dataset from the BingAds sponsored search auction marketplace. We show that the no-regret econometric methods perform comparable to state-of-the-art time-series machine learning methods when there is no co-variate shift, but significantly outperform machine learning methods when there is a co-variate shift between the training and test periods. This portrays the importance of using structural econometric approaches in predicting how players will respond to changes in the market. Moreover, we show that among structural econometric methods, approaches based on no-regret learning outperform more traditional, equilibrium-based, econometric methods that assume that players continuously best-respond to competition. Finally, we demonstrate how the prediction performance of the no-regret learning algorithms can be further improved by considering bidders who optimize a utility function with a visibility bias component.
Resource Augmentation
Venue: arXiv cs.DS 2020
Year: 2020

This chapter introduces resource augmentation, in which the performance of an algorithm is compared to the best-possible solution that is handicapped by less resources. We consider three case studies: online paging, with cache size as the resource; selfish routing, with capacity as the resource; and scheduling, with processor speed as the resource. Resource augmentation bounds also imply "loosely competitive" bounds, which show that an algorithm's performance is near-optimal for most resource levels.
Point-to-set distance functions for weakly supervised segmentation
Venue: arXiv cs.CV 2020
Year: 2020

When pixel-level masks or partial annotations are not available for training neural networks for semantic segmentation, it is possible to use higher-level information in the form of bounding boxes, or image tags. In the imaging sciences, many applications do not have an object-background structure and bounding boxes are not available. Any available annotation typically comes from ground truth or domain experts. A direct way to train without masks is using prior knowledge on the size of objects/classes in the segmentation. We present a new algorithm to include such information via constraints on the network output, implemented via projection-based point-to-set distance functions. This type of distance functions always has the same functional form of the derivative, and avoids the need to adapt penalty functions to different constraints, as well as issues related to constraining properties typically associated with non-differentiable functions. Whereas object size information is known to enable object segmentation from bounding boxes from datasets with many general and medical images, we show that the applications extend to the imaging sciences where data represents indirect measurements, even in the case of single examples. We illustrate the capabilities in case of a) one or more classes do not have any annotation; b) there is no annotation at all; c) there are bounding boxes. We use data for hyperspectral time-lapse imaging, object segmentation in corrupted images, and sub-surface aquifer mapping from airborne-geophysical remote-sensing data. The examples verify that the developed methodology alleviates difficulties with annotating non-visual imagery for a range of experimental settings.
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches
Venue: arXiv cs.CV 2020
Year: 2020

Zero-shot learning (ZSL) is commonly used to address the very pervasive problem of predicting unseen classes in fine-grained image classification and other tasks. One family of solutions is to learn synthesised unseen visual samples produced by generative models from auxiliary semantic information, such as natural language descriptions. However, for most of these models, performance suffers from noise in the form of irrelevant image backgrounds. Further, most methods do not allocate a calculated weight to each semantic patch. Yet, in the real world, the discriminative power of features can be quantified and directly leveraged to improve accuracy and reduce computational complexity. To address these issues, we propose a novel framework called multi-patch generative adversarial nets (MPGAN) that synthesises local patch features and labels unseen classes with a novel weighted voting strategy. The process begins by generating discriminative visual features from noisy text descriptions for a set of predefined local patches using multiple specialist generative models. The features synthesised from each patch for unseen classes are then used to construct an ensemble of diverse supervised classifiers, each corresponding to one local patch. A voting strategy averages the probability distributions output from the classifiers and, given that some patches are more discriminative than others, a discrimination-based attention mechanism helps to weight each patch accordingly. Extensive experiments show that MPGAN has significantly greater accuracy than state-of-the-art methods.
Our House is Our Glassy Castle: Challenges of Pervasive Computing in Private Spaces
Venue: arXiv cs.CY 2020
Year: 2020

Modern society is going through the transformation under the influence of Information Technologies. Internet of Things as one of the latest facet of it becoming more visible and widely spread. We wish to reflect and discuss the current concerns regarding its expansion. Our particular interests lie in the increasing of usability and comfortability through the unification of the IoT protocols and security measures. As well as addressing the privacy concerns and discussing the possible changings in the perception of privacy and personal space concepts.
Intracranial hemodynamics simulations: An efficient and accurate immersed boundary scheme
Venue: arXiv cs.CE 2020
Year: 2020

Computational fluid dynamics (CFD) studies have been increasingly used for blood flow simulations in intracranial aneurysms (ICAs). However, despite the continuous progress of body-fitted CFD solvers, generating a high quality mesh is still the bottleneck of the CFD simulation, and strongly affects the accuracy of the numerical solution. To overcome this challenge, which will allow preforming CFD simulations efficiently for a large number of aneurysm cases we use an Immersed Boundary (IB) method. The proposed scheme relies on Cartesian grids to solve the incompressible Navier-Stokes (N-S) equations, using a finite element solver, and Lagrangian points to discretize the immersed object. All grid generations are conducted through automated algorithms which require no user input. Consequently, we verify the proposed method by comparing our numerical findings (velocity values) with published experimental results. Finally, we test the ability of the scheme to efficiently handle hemodynamic simulations on complex geometries on a sample of four patient-specific intracranial aneurysms.
Detection and Performance Analysis for Non-Coherent DF Relay Networks with Optimized Generalized Differential Modulation
Venue: arXiv cs.IT 2020
Year: 2020

This paper studies the detection and performance analysis problems for a relay network with $N$ parallel decode-and-forward (DF) relays. Due to the distributed nature of this network, it is practically very challenging to fulfill the requirement of instantaneous channel state information for coherent detection. To bypass this requirement, we consider the use of non-coherent DF relaying based on a generalized differential modulation (GDM) scheme, in which transmission power allocation over the $M$-ary phase shift keying symbols is exploited when performing differential encoding. In this paper, a novel detector at the destination of such a non-coherent DF relay network is proposed. It is an accurate approximation of the state-of-the-art detector, called the almost maximum likelihood detector (AMLD), but the detection complexity is considerably reduced from $\mathcal{O}(M^2N)$ to $\mathcal{O}(MN)$. By characterizing the dominant error terms, we derive an accurate approximate symbol error rate (SER) expression. An optimized power allocation scheme for GDM is further designed based on this SER expression. Our simulation demonstrates that the proposed non-coherent scheme can perform close to the coherent counterpart as the block length increases. Additionally, we prove that the diversity order of both the proposed detector and the AMLD is exactly $\lceil N/2 \rceil + 1$. Extensive simulation results further verify the accuracy of our results in various scenarios.
Benchmarking Meta-heuristic Optimization
Venue: arXiv cs.NE 2020
Year: 2020

Solving an optimization task in any domain is a very challenging problem, especially when dealing with nonlinear problems and non-convex functions. Many meta-heuristic algorithms are very efficient when solving nonlinear functions. A meta-heuristic algorithm is a problem-independent technique that can be applied to a broad range of problems. In this experiment, some of the evolutionary algorithms will be tested, evaluated, and compared with each other. We will go through the Genetic Algorithm\, Differential Evolution, Particle Swarm Optimization Algorithm, Grey Wolf Optimizer, and Simulated Annealing. They will be evaluated against the performance from many points of view like how the algorithm performs throughout generations and how the algorithm's result is close to the optimal result. Other points of evaluation are discussed in depth in later sections.
Identification, Tracking and Impact: Understanding the trade secret of catchphrases
Venue: arXiv cs.DL 2020
Year: 2020

Understanding the topical evolution in industrial innovation is a challenging problem. With the advancement in the digital repositories in the form of patent documents, it is becoming increasingly more feasible to understand the innovation secrets -- "catchphrases" of organizations. However, searching and understanding this enormous textual information is a natural bottleneck. In this paper, we propose an unsupervised method for the extraction of catchphrases from the abstracts of patents granted by the U.S. Patent and Trademark Office over the years. Our proposed system achieves substantial improvement, both in terms of precision and recall, against state-of-the-art techniques. As a second objective, we conduct an extensive empirical study to understand the temporal evolution of the catchphrases across various organizations. We also show how the overall innovation evolution in the form of introduction of newer catchphrases in an organization's patents correlates with the future citations received by the patents filed by that organization. Our code and data sets will be placed in the public domain soon.
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction
Venue: arXiv cs.CV 2020
Year: 2020

Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence Magnetic Resonance Imaging (MRI) scans. Therefore, we propose unsupervised Medical Anomaly Detection Generative Adversarial Network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 L1 loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average L2 loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1,133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our Self-Attention MADGAN can detect AD on T1 scans at a very early stage, Mild Cognitive Impairment (MCI), with Area Under the Curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921.
Case Survey Studies in Software Engineering Research
Venue: arXiv cs.SE 2020
Year: 2020

Background: Given the social aspects of Software Engineering (SE), in the last twenty years, researchers from the field started using research methods common in social sciences such as case study, ethnography, and grounded theory. More recently, case survey, another imported research method, has seen its increasing use in SE studies. It is based on existing case studies reported in the literature and intends to harness the generalizability of survey and the depth of case study. However, little is known on how case survey has been applied in SE research, let alone guidelines on how to employ it properly. Aims: This article aims to provide a better understanding of how case survey has been applied in Software Engineering research. Method: To address this knowledge gap, we performed a systematic mapping study and analyzed 12 Software Engineering studies that used the case survey method. Results: Our findings show that these studies presented a heterogeneous understanding of the approach ranging from secondary studies to primary inquiries focused on a large number of instances of a research phenomenon. They have not applied the case survey method consistently as defined in the seminal methodological papers. Conclusions: We conclude that a set of clearly defined guidelines are needed on how to use case survey in SE research, to ensure the quality of the studies employing this approach and to provide a set of clearly defined criteria to evaluate such work.
Ordinary Differential Equation and Complex Matrix Exponential for Multi-resolution Image Registration
Venue: arXiv cs.CV 2020
Year: 2020

Autograd-based software packages have recently renewed interest in image registration using homography and other geometric models by gradient descent and optimization, e.g., AirLab and DRMIME. In this work, we emphasize on using complex matrix exponential (CME) over real matrix exponential to compute transformation matrices. CME is theoretically more suitable and practically provides faster convergence as our experiments show. Further, we demonstrate that the use of an ordinary differential equation (ODE) as an optimizable dynamical system can adapt the transformation matrix more accurately to the multi-resolution Gaussian pyramid for image registration. Our experiments include four publicly available benchmark datasets, two of them 2D and the other two being 3D. Experiments demonstrate that our proposed method yields significantly better registration compared to a number of off-the-shelf, popular, state-of-the-art image registration toolboxes.
Extending Concurrent Separation Logic to Enhance Modular Formalization
Venue: arXiv cs.LO 2020
Year: 2020

Nowadays, numerous services based on large-scale distributed systems have been developed to boost the convenience of human life. On the other side, it becomes a significant challenge to ensure the correctness and properties of these systems due to the complex and nested architecture. Although concurrent separation logic (CSL) has partially tackled the problem by specifying systems and verifying the correctness of them, it faces modularity issues. In this paper, we propose an extended concurrent separation logic (ECSL) to address the modularity issues of CSL with the support of the temporal extension, communication extension, environment extension, and nest extension. ECSL is capable of formalizing systems at different abstraction levels from memory management to architecture and protocol design with great modularity. Furthermore, we stick to unitarity and compatibility principles while developing ECSL.
Massive Access in Secure NOMA under Imperfect CSI: Security Guaranteed Sum-Rate Maximization with First-Order Algorithm
Venue: arXiv cs.IT 2020
Year: 2020

Non-orthogonal multiple access (NOMA) is a promising solution for secure transmission under massive access. However, in addition to the uncertain channel state information (CSI) of the eavesdroppers due to their passive nature, the CSI of the legitimate users may also be imperfect at the base station due to the limited feedback. Under both channel uncertainties, the optimal power allocation and transmission rate design for a secure NOMA scheme is currently not known due to the difficulty of handling the probabilistic constraints. This paper fills this gap by proposing novel transformation of the probabilistic constraints and variable decoupling so that the security guaranteed sum-rate maximization problem can be solved by alternatively executing branch-and-bound method and difference of convex programming. To scale the solution to a truly massive access scenario, a first-order algorithm with very low complexity is further proposed. Simulation results show that the proposed first-order algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant transmission scheme significantly improves the security guaranteed sum-rate compared to the orthogonal multiple access transmission and NOMA ignoring CSI uncertainty.